OrangeBot.AI Digest — 2026-01-12

Fullstack app framework for web, desktop, and mobile.

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Autonomous AI development loop for Claude Code with intelligent exit detection

Collection of publicly available IPTV channels from all over the world

real time face swap and one-click video deepfake with only a single image

The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra

Claude Code superpowers: core skills library

🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code support via MCP protocol. Ranked #1 in agent-based frameworks.

📘 Home Assistant User documentation

🎥 Command line media player

ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration

OpenProject is the leading open source project management software.

The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues. Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans -- using maps. In this work, we first equip the model Thinking with Map ability and formulate it as an agent-in-the-map loop. We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS). The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization. To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images. Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0\% to 22.1\% compared to Gemini-3-Pro with Google Search/Map grounded mode.

Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal nature of the physical world, where physics requires inferring hidden constraints (e.g., mass or energy) from visual elements. To address this, we propose MMFormalizer, which extends autoformalization beyond text by integrating adaptive grounding with entities from real-world mathematical and physical domains. MMFormalizer recursively constructs formal propositions from perceptually grounded primitives through recursive grounding and axiom composition, with adaptive recursive termination ensuring that every abstraction is supported by visual evidence and anchored in dimensional or axiomatic grounding. We evaluate MMFormalizer on a new benchmark, PhyX-AF, comprising 115 curated samples from MathVerse, PhyX, Synthetic Geometry, and Analytic Geometry, covering diverse multimodal autoformalization tasks. Results show that frontier models such as GPT-5 and Gemini-3-Pro achieve the highest compile and semantic accuracy, with GPT-5 excelling in physical reasoning, while geometry remains the most challenging domain. Overall, MMFormalizer provides a scalable framework for unified multimodal autoformalization, bridging perception and formal reasoning. To the best of our knowledge, this is the first multimodal autoformalization method capable of handling classical mechanics (derived from the Hamiltonian), as well as relativity, quantum mechanics, and thermodynamics. More details are available on our project page: MMFormalizer.github.io

A photorealistic and controllable 3D caricaturization framework for faces is introduced. We start with an intrinsic Gaussian curvature-based surface exaggeration technique, which, when coupled with texture, tends to produce over-smoothed renders. To address this, we resort to 3D Gaussian Splatting (3DGS), which has recently been shown to produce realistic free-viewpoint avatars. Given a multiview sequence, we extract a FLAME mesh, solve a curvature-weighted Poisson equation, and obtain its exaggerated form. However, directly deforming the Gaussians yields poor results, necessitating the synthesis of pseudo-ground-truth caricature images by warping each frame to its exaggerated 2D representation using local affine transformations. We then devise a training scheme that alternates real and synthesized supervision, enabling a single Gaussian collection to represent both natural and exaggerated avatars. This scheme improves fidelity, supports local edits, and allows continuous control over the intensity of the caricature. In order to achieve real-time deformations, an efficient interpolation between the original and exaggerated surfaces is introduced. We further analyze and show that it has a bounded deviation from closed-form solutions. In both quantitative and qualitative evaluations, our results outperform prior work, delivering photorealistic, geometry-controlled caricature avatars.

Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand this, we propose that effective and learnable Long CoT trajectories feature stable molecular-like structures in unified view, which are formed by three interaction types: Deep-Reasoning (covalent-like), Self-Reflection (hydrogen-bond-like), and Self-Exploration (van der Waals-like). Analysis of distilled trajectories reveals these structures emerge from Long CoT fine-tuning, not keyword imitation. We introduce Effective Semantic Isomers and show that only bonds promoting fast entropy convergence support stable Long CoT learning, while structural competition impairs training. Drawing on these findings, we present Mole-Syn, a distribution-transfer-graph method that guides synthesis of effective Long CoT structures, boosting performance and RL stability across benchmarks.

Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process, and often lead to undesirable behaviors such as shortcut exploitation and hallucinations. To address these limitations, we propose Citation-aware Rubric Rewards (CaRR), a fine-grained reward framework for deep search agents that emphasizes reasoning comprehensiveness, factual grounding, and evidence connectivity. CaRR decomposes complex questions into verifiable single-hop rubrics and requires agents to satisfy these rubrics by explicitly identifying hidden entities, supporting them with correct citations, and constructing complete evidence chains that link to the predicted answer. We further introduce Citation-aware Group Relative Policy Optimization (C-GRPO), which combines CaRR and outcome rewards for training robust deep search agents. Experiments show that C-GRPO consistently outperforms standard outcome-based RL baselines across multiple deep search benchmarks. Our analysis also validates that C-GRPO effectively discourages shortcut exploitation, promotes comprehensive, evidence-grounded reasoning, and exhibits strong generalization to open-ended deep research tasks. Our code and data are available at https://github.com/THUDM/CaRR.

Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes. However, access to real systems is often restricted; LLM-simulated environments are prone to hallucinations and inconsistencies; and manually built sandboxes are hard to scale. In this paper, we propose EnvScaler, an automated framework for scalable tool-interaction environments via programmatic synthesis. EnvScaler comprises two components. First, SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation. Then, ScenGenerator generates multiple task scenarios and rule-based trajectory validation functions for each environment. With EnvScaler, we synthesize 191 environments and about 7K scenarios, and apply them to Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models. Results on three benchmarks show that EnvScaler significantly improves LLMs' ability to solve tasks in complex environments involving multi-turn, multi-tool interactions. We release our code and data at https://github.com/RUC-NLPIR/EnvScaler.

In this report, we introduce the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, the latest extensions of the Qwen family built on the Qwen3-VL foundation model. Together, they provide an end-to-end pipeline for high-precision multimodal search by mapping diverse modalities, including text, images, document images, and video, into a unified representation space. The Qwen3-VL-Embedding model employs a multi-stage training paradigm, progressing from large-scale contrastive pre-training to reranking model distillation, to generate semantically rich high-dimensional vectors. It supports Matryoshka Representation Learning, enabling flexible embedding dimensions, and handles inputs up to 32k tokens. Complementing this, Qwen3-VL-Reranker performs fine-grained relevance estimation for query-document pairs using a cross-encoder architecture with cross-attention mechanisms. Both model series inherit the multilingual capabilities of Qwen3-VL, supporting more than 30 languages, and are released in 2B and 8B parameter sizes to accommodate diverse deployment requirements. Empirical evaluations demonstrate that the Qwen3-VL-Embedding series achieves state-of-the-art results across diverse multimodal embedding evaluation benchmarks. Specifically, Qwen3-VL-Embedding-8B attains an overall score of 77.8 on MMEB-V2, ranking first among all models (as of January 8, 2025). This report presents the architecture, training methodology, and practical capabilities of the series, demonstrating their effectiveness on various multimodal retrieval tasks, including image-text retrieval, visual question answering, and video-text matching.

Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer from a severe Execution Bottleneck, as hypothesis evaluation relies strictly on expensive physical execution. To bypass these physical constraints, we internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. In this work, we formalize the task of Data-centric Solution Preference and construct a comprehensive corpus of 18,438 pairwise comparisons. We demonstrate that LLMs exhibit significant predictive capabilities when primed with a Verified Data Analysis Report, achieving 61.5% accuracy and robust confidence calibration. Finally, we instantiate this framework in FOREAGENT, an agent that employs a Predict-then-Verify loop, achieving a 6x acceleration in convergence while surpassing execution-based baselines by +6%. Our code and dataset will be publicly available soon at https://github.com/zjunlp/predict-before-execute.

Preference tuning aligns pretrained language models to human judgments of quality, helpfulness, or safety by optimizing over explicit preference signals rather than likelihood alone. Prior work has shown that preference-tuning degrades performance and reduces helpfulness when evaluated outside the training domain. However, the extent to which adaptation strategies mitigate this domain shift remains unexplored. We address this challenge by conducting a comprehensive and systematic study of alignment generalization under domain shift. We compare five popular alignment objectives and various adaptation strategies from source to target, including target-domain supervised fine-tuning and pseudo-labeling, across summarization and question-answering helpfulness tasks. Our findings reveal systematic differences in generalization across alignment objectives under domain shift. We show that adaptation strategies based on pseudo-labeling can substantially reduce domain-shift degradation

Recent advances in large language models (LLMs) enable agentic systems trained with reinforcement learning (RL) over multi-turn interaction trajectories, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token budgets and memory usage. We introduce AgentOCR, a framework that exploits the superior information density of visual tokens by representing the accumulated observation-action history as a compact rendered image. To make multi-turn rollouts scalable, AgentOCR proposes segment optical caching. By decomposing history into hashable segments and maintaining a visual cache, this mechanism eliminates redundant re-rendering. Beyond fixed rendering, AgentOCR introduces agentic self-compression, where the agent actively emits a compression rate and is trained with compression-aware reward to adaptively balance task success and token efficiency. We conduct extensive experiments on challenging agentic benchmarks, ALFWorld and search-based QA. Remarkably, results demonstrate that AgentOCR preserves over 95\% of text-based agent performance while substantially reducing token consumption (>50\%), yielding consistent token and memory efficiency. Our further analysis validates a 20x rendering speedup from segment optical caching and the effective strategic balancing of self-compression.

Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first large-scale Visual Autoregressive (VAR) framework for video generation that combines multi-scale next-frame prediction with autoregressive modeling. VideoAR disentangles spatial and temporal dependencies by integrating intra-frame VAR modeling with causal next-frame prediction, supported by a 3D multi-scale tokenizer that efficiently encodes spatio-temporal dynamics. To improve long-term consistency, we propose Multi-scale Temporal RoPE, Cross-Frame Error Correction, and Random Frame Mask, which collectively mitigate error propagation and stabilize temporal coherence. Our multi-stage pretraining pipeline progressively aligns spatial and temporal learning across increasing resolutions and durations. Empirically, VideoAR achieves new state-of-the-art results among autoregressive models, improving FVD on UCF-101 from 99.5 to 88.6 while reducing inference steps by over 10x, and reaching a VBench score of 81.74-competitive with diffusion-based models an order of magnitude larger. These results demonstrate that VideoAR narrows the performance gap between autoregressive and diffusion paradigms, offering a scalable, efficient, and temporally consistent foundation for future video generation research.

As Large Language Models (LLMs) are increasingly deployed in real-world settings, correctness alone is insufficient. Reliable deployment requires maintaining truthful beliefs under contextual perturbations. Existing evaluations largely rely on point-wise confidence like Self-Consistency, which can mask brittle belief. We show that even facts answered with perfect self-consistency can rapidly collapse under mild contextual interference. To address this gap, we propose Neighbor-Consistency Belief (NCB), a structural measure of belief robustness that evaluates response coherence across a conceptual neighborhood. To validate the efficiency of NCB, we introduce a new cognitive stress-testing protocol that probes outputs stability under contextual interference. Experiments across multiple LLMs show that the performance of high-NCB data is relatively more resistant to interference. Finally, we present Structure-Aware Training (SAT), which optimizes context-invariant belief structure and reduces long-tail knowledge brittleness by approximately 30%. Code will be available at https://github.com/zjunlp/belief.

Recent advancements in video generation have enabled the development of ``world models'' capable of simulating potential futures for robotics and planning. However, specifying precise goals for these models remains a challenge; text instructions are often too abstract to capture physical nuances, while target images are frequently infeasible to specify for dynamic tasks. To address this, we introduce Goal Force, a novel framework that allows users to define goals via explicit force vectors and intermediate dynamics, mirroring how humans conceptualize physical tasks. We train a video generation model on a curated dataset of synthetic causal primitives-such as elastic collisions and falling dominos-teaching it to propagate forces through time and space. Despite being trained on simple physics data, our model exhibits remarkable zero-shot generalization to complex, real-world scenarios, including tool manipulation and multi-object causal chains. Our results suggest that by grounding video generation in fundamental physical interactions, models can emerge as implicit neural physics simulators, enabling precise, physics-aware planning without reliance on external engines. We release all datasets, code, model weights, and interactive video demos at our project page.

Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit a range of human biases. Behavioral biases can lead to instability and uncertainty in decision-making, particularly when processing financial information. However, existing research on LLM bias has mainly focused on direct questioning or simplified, general-purpose settings, with limited consideration of the complex real-world financial environments and high-risk, context-sensitive, multilingual financial misinformation detection tasks (\mfmd). In this work, we propose \mfmdscen, a comprehensive benchmark for evaluating behavioral biases of LLMs in \mfmd across diverse economic scenarios. In collaboration with financial experts, we construct three types of complex financial scenarios: (i) role- and personality-based, (ii) role- and region-based, and (iii) role-based scenarios incorporating ethnicity and religious beliefs. We further develop a multilingual financial misinformation dataset covering English, Chinese, Greek, and Bengali. By integrating these scenarios with misinformation claims, \mfmdscen enables a systematic evaluation of 22 mainstream LLMs. Our findings reveal that pronounced behavioral biases persist across both commercial and open-source models. This project will be available at https://github.com/lzw108/FMD.

This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orient Anything V1, which defines orientation via a single unique front face, V2 extends this capability to handle objects with diverse rotational symmetries and directly estimate relative rotations. These improvements are enabled by four key innovations: 1) Scalable 3D assets synthesized by generative models, ensuring broad category coverage and balanced data distribution; 2) An efficient, model-in-the-loop annotation system that robustly identifies 0 to N valid front faces for each object; 3) A symmetry-aware, periodic distribution fitting objective that captures all plausible front-facing orientations, effectively modeling object rotational symmetry; 4) A multi-frame architecture that directly predicts relative object rotations. Extensive experiments show that Orient Anything V2 achieves state-of-the-art zero-shot performance on orientation estimation, 6DoF pose estimation, and object symmetry recognition across 11 widely used benchmarks. The model demonstrates strong generalization, significantly broadening the applicability of orientation estimation in diverse downstream tasks.

微软想要让 Windows 的 AI 功能更醒目。根据 Windows 11 的预览版信息，它正在测试在资源管理器中集成 Copilot 的新功能，不是在右键菜单中添加一个“Ask Copilot”按钮，而是集成在资源管理器中，位于侧边栏或类似详细信息/预览窗格界面中。

根据 Netblocks 以及 Cloudflare Rader 的监测，伊朗全国断网四天。自 2025 年 12 月起，伊朗发生了一系列抗议活动，起因是民众对通货膨胀飙升、食品价格上涨以及伊朗里亚尔大幅贬值感到不满。示威活动最初由店主和市场商贩发起，进入新年后，抗议规模日益扩大。维基百科显示，目前有至少 2000 人死亡，逾万人被捕。

全球首台超临界二氧化碳发电机组“超碳一号”在贵州六盘水首钢水钢集团投入商业运行，这是全球范围内首次将超临界二氧化碳发电技术从实验室推向商业落地。不论是火电还是核电，原理都类似于“烧开水”，就是用热量将水变为水蒸气，推动汽轮机转动来发电。但超临界二氧化碳发电技术是一种革新型热电转换技术。这一技术是把温度超过 31 摄氏度、压力升高至 73 个大气压以上环境中的超临界二氧化碳作为循环工质，将其送进发电系统里，再通过压缩机和换热器提高超临界二氧化碳的压力和温度，让高温高压的二氧化碳推动透平旋转，进而产生电能。相比现役烧结余热蒸汽发电技术，“超碳一号”发电效率提升 85% 以上，净发电量提升 50% 以上。

Linus Torvalds 在 GitHub 上公开了名为 AudioNoise 的随机数字音效个人项目，他在 README 文件中透露使用了 Google 的 AI 驱动 IDE 工具 Antigravity 进行了辅助编程——或者叫 Vibe Coding。Antigravity 是 Windsurf 的分支，而 Windsurf 则是微软 VS Code 的分支。

因为大量用户在 APP 上与 AI 智能体“聊黄”，APP 的主要开发和运营者被追究了刑责。2025 年 9 月，上海市徐汇区人民法院一审判决，两名被告人犯制作淫秽物品牟利罪，分别获刑四年、一年半。此案成为国内首起 AI 服务提供者涉黄获刑的案件。案涉 APP Alien Chat（AC）是一款 AI 伴侣聊天应用，定位是为年轻群体提供亲密陪伴和情感支持。用户在 AC 注册会员后，可与 AI 聊天。判决书披露，AC App 手机注册用户 11.6 万人，其中付费用户 2.4 万人。截至案发，共收取会员充值费 363 万余元。两名被告人不服判决提出上诉，案件二审将于 1 月 14 日在上海市第一中级人民法院开庭。

在本该白雪皑皑的季节，喜马拉雅山却是光秃秃的，因为今年冬季的降雪量大幅减少。相比 1980-2020 年之间的平均降雪量，过去五年喜马拉雅山多数年份的降雪量都出现了下降。全球的气温上升也意味着少量积雪会很快融化。气象学家表示积雪减少不仅改变喜马拉雅山面貌，还会影响当地数亿人的生活和生态系统。融雪是该地区主要的淡水来源。印度气象部门记录显示 12 月印度北部几乎所有地区都没有降雨和降雪。根据数据集 ERA-5（European Centre for Medium-Range Weather Forecasts Reanalysis），相比 40 年长期平均水平（1980-2020 年），喜马拉雅山西北部的降雪量过去五年减少了 25%。

Gentoo Linux 项目回顾了 2025 年展望了 2026 年。开发者表示，目前微软旗下的 GitHub 不断强行在代码库中推销使用其 AI 功能 Copilot，Gentoo 考虑计划将库镜像和 pull request 迁移到 Codeberg。Codeberg 是基于 Forgejo 的服务，由德国柏林的一家非营利组织维护。Gentoo 将继续托管自己的主 git 库、bug 等基础设施，这些都没有计划改变。

加拿大科幻小说作家、平台垃圾化（enshittification）一词的发明者 Cory Doctorow 在《卫报》上发表文章，重复了他在 39C3 会议上主张的一个观点：合法化逆向工程有助于终结平台垃圾化。他说，美国公司的有缺陷产品之所以没有失去吸引力的一大原因是法律禁止逆向工程——或者叫反规避。而各国法律之所以包含反规避条款是美国对其贸易伙伴的强制性要求。而逆向工程是修改现有产品使其更好为用户服务的必要前提。在废除法律中的反规避条款前，我们无法对美国的云软件进行逆向工程，不管是数据库或字处理器等软件，还是拖拉机之类的硬件，在废除后我们才能将更自由开放可审计的代码去取代美国的私有代码，更好的维护数字主权。

因为 X 平台的聊天机器人 Grok 能未经女性和儿童同意制作性暴露的类 deepfake 图像，马来西亚和印尼宣布屏蔽 Grok。马来西亚通信监管机构 Communications and Multimedia Commission 周日表示它向 X 发去通知要求加强对 Grok 的管理，但 X 未能在回复中解决问题。 印尼也要求 X 对 Grok 使用做出说明，认为 Grok 生成露骨色情内容侵犯了人权、尊严和网络安全。英国的通信监管机构 Ofcom 也将对如何处理 Grok 做出决定。

根据 Netblocks 以及 Cloudflare Rader 的监测，伊朗断网三天。自 2025 年 12 月起，伊朗发生了一系列抗议活动，起因是民众对通货膨胀飙升、食品价格上涨以及伊朗里亚尔大幅贬值感到不满。示威活动最初由店主和市场商贩发起，进入新年后，抗议规模日益扩大。维基百科显示，目前有至少 2000 人死亡，但该数字难以核实。

微生物学在 19 世纪末进入黄金时代，研究人员发现了结核病、霍乱、伤寒等十几种疾病的细菌病原体。抗生素在 20 世纪中期进入黄金时代。如今疫苗的研发看起来也进入到了它的黄金时代，问题是美国的现任卫生部长是反疫苗者。在 2020 年代的前五年，研究人员研发出了新冠、疟疾、呼吸道合胞病毒(RSV)和基孔肯雅（Chikungunya）四种不同疾病的有效疫苗。疫苗的加速研发受益于过去两个世纪打造的基础。Edward Jenner 在 1796 年研发出天花疫苗是一次意外，他本人并不理解原理。Louis Pasteur 在 90 年后弄清楚了疫苗的原理——减毒和灭活——然后将其应用于治疗其它疾病。一代又一代的科学家构建起了配套的基础设施：用于细菌培养的培养皿、体外维持动物细胞存活的技术、用于工业生产的生物反应器、灭菌设备和冷链物流。如今相关工具更加强大。冷冻电镜能逐个原子揭示病毒蛋白的结构，这项技术直接促成了 RSV 疫苗的研发。基因组测序的成本从 2001 年约 1 亿美元降至 2014 年的低于 1000 美元。Katalin Kariko、Drew Weissman 等人改进的 mRNA 平台让疫苗的重新设计能在几周而不是数年内完成。未来可能出现更多的疫苗突破。然而突破能否实现取决于持续的投入。

为减少对美国科技公司的依赖，欧盟将注意力转向了开源软件，它正在征询欧洲开源社区、商界、学界和公共机构对开源相关问题的意见，截止日期 2 月 3 日。欧盟表示在数字领域面临严重依赖非欧盟国家(aka 美国)的问题。对美国的依赖减少了用户选择，削弱了欧盟企业的竞争力，因为无法控制数字基础设施，可能会引发供应链安全问题。过去几年开源作为一种可自由使用、修改和再分发的公共产品的潜力受到了广泛认可，开源软件能成为私有解决方案的有效替代。开源能增强用户自主权，帮助重新掌控和提升数字基础设施的韧性。

负责评选诺贝尔和平奖的挪威诺贝尔委员会通过官网发表了一份简短声明，它收到了有关诺贝尔和平奖得主身份是否永久有效的置评请求。声明表示，事实明确且早有定论：一旦诺贝尔奖公布，即不可撤销、不可分享，也不可转让他人。该决定为最终决定，永久有效。

根据 Netblocks 的监测，伊朗全国范围内断网，至今已持续近 48 小时。自 2025 年 12 月起，伊朗发生了一系列抗议活动，起因是民众对通货膨胀飙升、食品价格上涨以及伊朗里亚尔大幅贬值感到不满。示威活动最初由店主和市场商贩发起，进入新年后，抗议规模日益扩大。报道称至少逾两百人死亡，逾两千人被捕。由于互联网被切断，以及 Starlink 被干扰而无法访问，当地具体情况未知。

人类何时开始利用沾了有毒物质的武器去猎杀猎物？根据发表在《Science Advances》期刊上的一项研究，瑞典斯德哥尔摩大学（Stockholm University）的考古学家在南非出土的距今有 6 万年历史的箭头上发现了毒药残留。此前最早的毒药使用痕迹上溯至 3.5 万年前。这项发现进一步证实早期智人的认知能力在老练上已接近现代人类。为了在箭矢上涂抹毒药，猎人需要了解当地植物及其效用，能制作出剂量正确的特制武器。研究人员发现，有 6 万年历史的石英箭头端非常小，表明可能是为了有效将毒药注入伤口而设计的，不是为了造成钝器伤。研究人员发现了两种有毒化合物：buphanidrine 和 epibuphanisine，很可能来自于当地植物 Boophone disticha。这种植物今天还是当地猎人常用的毒药来源，被用于减缓猎物速度，而不是通过致命一击将其杀死。